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Remote Sens. 2016, 8(9), 774; doi:10.3390/rs8090774

Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

1
International Institute for Applied Systems Analysis, Laxenburg A2361, Austria
2
Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp SE-23053, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Cidália Costa Fonte, Parth Sarathi Roy, Clement Atzberger and Prasad S. Thenkabail
Received: 29 July 2016 / Revised: 2 September 2016 / Accepted: 12 September 2016 / Published: 20 September 2016
(This article belongs to the Special Issue Citizen Science and Earth Observation)
View Full-Text   |   Download PDF [845 KB, uploaded 20 September 2016]   |  

Abstract

The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications. View Full-Text
Keywords: crowdsourcing; citizen science; data quality; Tobler’s Law; local knowledge; remote sensing; land cover; cropland; volunteered geographical information crowdsourcing; citizen science; data quality; Tobler’s Law; local knowledge; remote sensing; land cover; cropland; volunteered geographical information
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Salk, C.; Sturn, T.; See, L.; Fritz, S. Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task. Remote Sens. 2016, 8, 774.

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